Binaural multi-microphone noise reduction methods aim at noise suppression while preserving the spatial impression of the acoustic scene. Recently, a new binaural speech enhancement method was proposed which chooses per timefrequency (TF) tile either the enhanced target or a suppressed noisy version. The selection between the two is based on the input SNR per TF tile. In this paper we modify this method such that the selection mechanism is based on the output SNR. The proposed modification of deciding which TF tile is target-or
noise-dominated leads to choices, which are better aligned with simultaneous masking properties of the auditory system, and, hence, improves the performance over the initial version of the algorithm.@en

Binaural beamformers (BFs) aim to reduce the output noise power while simultaneously preserving the binaural cues of all sources. Typically, the latter is accomplished via
constraints relating the output and input interaural transfer functions (ITFs). The ITF is a function of the corresponding relative acoustic transfer function (RATF), which implies that RATF estimates of all sources in the acoustic scene are required. Here, we propose an alternative way to approximately preserve the binaural cues of the entire acoustic scene without estimating RATFs. We propose to preserve the binaural cues of all sources with a set of fixed pre-determined RATFs distributed around the head. Two recently proposed binaural BFs are evaluated in the context of using pre-determined RATFs and compared to the binaural minimum variance distrortionless response BF which can only preserve the binaural cues of the target.@en

We propose a new multi-microphone noise reduction technique for binaural cue preservation of the desired source and the interferers. This method is based on the linearly constrained minimum variance (LCMV) framework, where the constraints are used for the binaural cue preservation of the desired source and of multiple interferers. In this framework there is a trade-off between noise reduction and binaural cue preservation. The more constraints the LCMV uses for preserving binaural cues, the less degrees of freedom can be used for noise suppression. The recently presented binaural LCMV (BLCMV) method and the optimal BLCMV (OBLCMV) method require two constraints per interferer and introduce an additional interference rejection parameter. This unnecessarily reduces the degrees of freedom, available for noise reduction, and negatively influences the trade-off between noise reduction and binaural cue preservation. With the proposed method, binaural cue preservation is obtained using just a single constraint per interferer without the need of an interference rejection parameter. The proposed method can simultaneously achieve noise reduction and perfect binaural cue preservation of more than twice as many interferers as the BLCMV, while the OBLCMV can preserve the binaural cues of only one interferer.@en

In the recent years, there have been an increasing amount of researches aiming at optimal beamforming with ad hoc microphone arrays, mostly fusion-center-based schemes. However, huge computational complexities and communication overheads impede many of these algorithms from being useful in practice. In this paper, we propose a low-footprint optimization approach to reduce the convergence time and overheads for the distributed beamforming problem. We transcribe the pseudo-coherence-based beamforming which is insightful for taking into account the nature of speech. We formulate the distributed minimum variance distortionless response beamformer using the primal-dual method of multipliers. Our experiments confirm the fast convergence using the proposed distributed algorithm. It is also shown how a hard limit on the number of iterations affects the performance of the array in noise and interference suppression.@en